Prediction model training management system, method of the same, master apparatus and slave apparatus for the same

ABSTRACT

Disclosed are a system and method for training and managing a prediction model, and a master apparatus and a slave apparatus for the same. there is provided a system for training and managing a prediction model, the system including a master apparatus configured to generate a prediction model, train the prediction model, and obtain the trained prediction model; and a slave apparatus configured to collect data, transmit the data to the master apparatus, receive the prediction model or the trained prediction model from the master apparatus, and operate based on the prediction model or the trained prediction model. The master apparatus is further configured to generate the prediction model or train the prediction model based on the data transmitted from the slave apparatus.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit under 35 USC § 119(a) of KoreanPatent Application No. 10-2018-0122683 filed on Oct. 15, 2018 in theKorean Intellectual Property Office, the entire disclosure of which isincorporated herein by reference for all purposes.

BACKGROUND 1. Field

At least one example embodiment relates to a system and method fortraining and managing a prediction model, and a master apparatus and aslave apparatus for the same.

2. Description of Related Art

In recent years, artificial intelligence is used in various fields.Specifically, the artificial intelligence is applied in various fields,such as, for example, determining a next number of Go, recognizingcharacters, and translating one language to another language.

Artificial intelligence technology includes a machine learning. Here,the machine learning refers to a process of training a predetermined(or, alternatively, desired) algorithm based on a variety of informationonce the algorithm is given, and obtaining the trained algorithm basedon a training result. That is, the machine learning refers to a processin which an apparatus, for example, a computer, generates rules byitself through direct training.

Meanwhile, in performing machine learning for intelligent management ofnetwork resources, a training process of an algorithm, for example, aprediction model, for such intent has some issues. Specifically, it isdifficult to determine an appropriate learning model among various typesof learning models, for example, a multilayer perceptron (MLP), a deepneural network (DNN), and a convolutional neural network (CNN). Also,conducting a search from among various types of learning models usingdifferent hyper parameters may cause a combination issue. Further, it isdifficult to adjust and determine parameters, for example, a depth of aselected learning model, a type thereof, and a learning rate forenhancing accuracy of a prediction rate. Accordingly, there is a needfor a method that may determine and obtain an appropriate predictionmodel for artificial management of network resources in technical,industrial, and economical aspects.

SUMMARY

At least one example embodiment provides a system and method fortraining and managing a prediction model, and a master apparatus and aslave apparatus for the same that may automate obtaining and training ofan appropriate prediction model.

According to at least one example embodiment, there are provided asystem and method for training and managing a prediction model, and amaster apparatus and a slave apparatus for the same.

According to an aspect of at least one example embodiment, there isprovided a system for training and managing a prediction model, thesystem including a master apparatus configured to generate a predictionmodel, train the prediction model, and obtain the trained predictionmodel; and a slave apparatus configured to collect data, transmit thedata to the master apparatus, receive the prediction model or thetrained prediction model from the master apparatus, and operate based onthe prediction model or the trained prediction model. The masterapparatus is further configured to generate the prediction model ortrain the prediction model based on the data transmitted from the slaveapparatus.

The prediction model may include a first prediction model configured toobtain a prediction result about a class corresponding to input data;and a second prediction model configured to receive a result of thefirst prediction model and obtain a prediction result corresponding tothe result of the first prediction model.

The first prediction model may include a first algorithm to which datais input and a second algorithm to which an output result of the firstalgorithm is input.

Each of the first algorithm and the second algorithm may include atleast one of machine learning algorithms including at least one of amultilayer perceptron (MLP), a deep neural network (DNN), aconvolutional neural network (CNN), a recurrent neural network (RNN), aconvolutional recurrent neural network (CRNN), a deep belief network(DBN), and a deep Q-network.

The input data may be a tensor in which data is arranged in amultidimensional form.

The tensor may include a type of a feature and a data point around datato be predicted.

The slave apparatus may be further configured to train the predictionmodel or the trained prediction model independently of the masterapparatus and transmit the training result to the master apparatus.

The master apparatus may be further configured to update the trainedprediction model based on the training result.

At least one of the master apparatus and the slave apparatus may includea data processing configured to perform at least one processing of thecollected data and generate a data set based on the collected data; amodel generator configured to generate at least one prediction model; amodel trainer configured to train the at least one prediction modelgenerated by the model generator based on the data or the data settransferred by the data processing; a predictor configured to obtain aprediction result using at least one of the prediction model generatedby the model generator and the prediction model trained by the modeltrainer; and an artificial intelligence manager configured to controland manage at least one of the data processing, the model generator, themodel trainer, and the predictor.

According to an aspect of at least one example embodiment, there isprovided a master apparatus including a communicator configured tocommunicably connect to a slave apparatus and receive data from theslave apparatus; and a processor configured to generate a predictionmodel, train the prediction model, and obtain the trained predictionmodel. The communicator is further configured to transmit the predictionmodel or the trained prediction model to the slave apparatus.

The processor may include a data processing configured to perform atleast one processing of collected data and generate a data set based onthe collected data; a model generator configured to generate at leastone prediction model; and a model trainer configured to train the atleast one prediction model generated by the model generator based on thedata or the data set transferred by the data processing.

The prediction model may include a first prediction model configured toobtain a prediction result about a class corresponding to input data;and a second prediction model configured to receive a result of thefirst prediction model and obtain a prediction result corresponding tothe result of the first prediction model.

The first prediction model may include a first algorithm to which datais input and a second algorithm to which an output result of the firstalgorithm is input.

Each of the first algorithm and the second algorithm may include atleast one of machine learning algorithms including at least one of anMLP, a DNN, a CNN, an RNN, a CRNN, a DBN, and a deep Q-network.

The processor may further include a predictor configured to obtain aprediction result using at least one of the prediction model generatedby the model generator and the prediction model trained by the modeltrainer.

The processor may further include an artificial intelligence managerconfigured to control and manage at least one of the data processing,the model generator, the model trainer, and the predictor.

According to an aspect of at least one example embodiment, there isprovided a slave apparatus including a communicator configured toreceive a prediction model or a trained prediction model from a masterapparatus; and a processor configured to generate a control commandbased on the prediction model or the trained prediction model. Theprocessor is further configured to train the prediction model or thetrained prediction model, and the communicator is further configured totransmit the training result to the master apparatus.

The prediction model may include a first prediction model configured toobtain a prediction result about a class corresponding to input data;and a second prediction model configured to receive a result of thefirst prediction model and obtain a prediction result corresponding tothe result of the first prediction model.

According to an aspect of at least one example embodiment, there isprovided a method of training and managing a prediction model, themethod including collecting, by at least one of a master apparatus and aslave apparatus, data; generating, by at least one of the masterapparatus and the slave apparatus, a prediction model based on the data;training, by at least one of the master apparatus and the slaveapparatus, the prediction model and obtaining the trained predictionmodel; and performing, by at least one of the master apparatus and theslave apparatus, a prediction based on at least one of the predictionmodel and the trained prediction model. The prediction model includes afirst prediction model configured to obtain a prediction result about aclass corresponding to input data; and a second prediction modelconfigured to receive a result of the first prediction model and obtaina prediction result corresponding to the result of the first predictionmodel.

The first prediction model may include a first algorithm to which datais input, and a second algorithm to which an output result of the firstalgorithm is input. The first algorithm and the second algorithm mayinclude at least one of machine learning algorithms including at leastone of an MLP, a DNN, a CNN, an RNN, a CRNN, a DBN, and a deepQ-network.

According to some example embodiments, there are provided a system andmethod for training and managing a prediction model, and a masterapparatus and a slave apparatus for the same that may automaticallyobtain a prediction model most appropriate for various types ofpredictions and train the obtained prediction model.

Also, according to some example embodiments, there are provided a systemand method for training and managing a prediction model, and a masterapparatus and a slave apparatus for the same that may relatively easilyretrieve and select an optimal prediction model from among a pluralityof prediction models.

Also, according to some example embodiments, there are provided a systemand method for training and managing a prediction model, and a masterapparatus and a slave apparatus for the same that enable a system havingrelatively low complexity to obtain an optimal prediction model fromamong a plurality of prediction models, thereby enhancing a systemconstruction in an economical aspect.

Also, according to some example embodiments, there are provided a systemand method for training and managing a prediction model, and a masterapparatus and a slave apparatus for the same that may appropriatelyobtain a prediction model for predicting demand of a user and mayperform intelligent management of network resources based on theobtained prediction model, thereby enhancing the resource use efficiencyof a system.

Also, according to some example embodiments, there are provided a systemand method for training and managing a prediction model, and a masterapparatus and a slave apparatus for the same that may enhance theaccuracy of an obtained prediction model.

Also, according to some example embodiments, there are provided a systemand method for training and managing a prediction model, and a masterapparatus and a slave apparatus for the same that may be applied to, forexample, various types of electronic and mechanical devices,applications, and various types of services such as network management,in various application fields, such that each of the variousapparatuses, applications, and services may easily and appropriatelyobtain a prediction model required for a corresponding operation.

Other features and aspects will be apparent from the following detaileddescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

These and/or other aspects, features, and advantages of the inventionwill become apparent and more readily appreciated from the followingdescription of example embodiments, taken in conjunction with theaccompanying drawings of which:

FIG. 1 illustrates an example of a system for training and managing aprediction model according to an example embodiment;

FIG. 2 is a block diagram illustrating an example of a system fortraining and managing a prediction model according to an exampleembodiment;

FIG. 3 is a block diagram illustrating an example of a master apparatusaccording to an example embodiment;

FIG. 4 illustrates an example of obtaining a prediction result accordingto an example embodiment;

FIG. 5 illustrates an example of sample data according to an exampleembodiment;

FIG. 6 illustrates an example of a method of training and managing aprediction model according to an example embodiment; and

FIG. 7 is a flowchart illustrating an example of a process of obtaininga prediction result according to an example embodiment.

DETAILED DESCRIPTION

Hereinafter, some example embodiments will be described in detail withreference to the accompanying drawings. Regarding the reference numeralsassigned to the elements in the drawings, it should be noted that thesame elements will be designated by the same reference numerals,wherever possible, even though they are shown in different drawings.Also, in the description of embodiments, detailed description ofwell-known related structures or functions will be omitted when it isdeemed that such description will cause ambiguous interpretation of thepresent disclosure.

The following detailed structural or functional description of exampleembodiments is provided as an example only and various alterations andmodifications may be made to the example embodiments. Accordingly, theexample embodiments are not construed as being limited to the disclosureand should be understood to include all changes, equivalents, andreplacements within the technical scope of the disclosure.

Unless the context clearly indicates otherwise, like reference numeralsrefer to like elements used throughout. Also, components used herein,such as, for example, terms ‘-unit/module’, etc., may be implemented assoftware and/or hardware. Depending on example embodiments, eachcomponent with ‘-unit/module’, etc., may be implemented as a singlepiece of software, hardware and/or a desired part, and also may beimplemented as a plurality of pieces of software, hardware, and/ordesired parts.

It should be noted that if it is described that one component is“connected”, “coupled”, or “joined” to another component, a thirdcomponent may be “connected”, “coupled”, and “joined” between the firstand second components, although the first component may be directlyconnected, coupled, or joined to the second component. On the contrary,it should be noted that if it is described that one component is“directly connected”, “directly coupled”, or “directly joined” toanother component, a third component may be absent. Expressionsdescribing a relationship between components, for example, “between”,directly between”, or “directly neighboring”, etc., should beinterpreted to be alike.

The singular forms “a”, “an”, and “the” are intended to include theplural forms as well, unless the context clearly indicates otherwise. Itwill be further understood that the terms “comprises/comprising” and/or“includes/including” when used herein, specify the presence of statedfeatures, integers, steps, operations, elements, and/or components, butdo not preclude the presence or addition of one or more other features,integers, steps, operations, elements, components and/or groups thereof.

Terms, such as first, second, and the like, may be used herein todescribe components. Each of these terminologies is not used to definean essence, order or sequence of a corresponding component but usedmerely to distinguish the corresponding component from othercomponent(s). For example, a first component may be referred to as asecond component, and similarly the second component may also bereferred to as the first component. As used herein, the term “and/or”includes any and all combinations of one or more of the associatedlisted items.

Unless otherwise defined, all terms, including technical and scientificterms, used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this disclosure pertains. Terms,such as those defined in commonly used dictionaries, are to beinterpreted as having a meaning that is consistent with their meaning inthe context of the relevant art, and are not to be interpreted in anidealized or overly formal sense unless expressly so defined herein.

Hereinafter, a master apparatus and a slave apparatus for training andmanaging a prediction model and a system for training and managing aprediction model including the master apparatus and the slave apparatuswill be described with reference to FIGS. 1 through 6.

FIG. 1 illustrates an example of a system (hereinafter, also referred toas a prediction model training management system) for training andmanaging a prediction according to an example embodiment.

Referring to FIG. 1, a prediction model training management system 1 mayinclude a master node (hereinafter, referred to as a master apparatus)100 and a slave node (hereinafter, referred to as a slave apparatus) 200configured to communicate with the master apparatus 100.

The master apparatus 100 may be configured to generate a predictionmodel based on the collected data, train the prediction model, andselect an optimal model. Here, the prediction model includes a modelused to perform a prediction desired by a designer, for example, aprediction of user demand for a product, a service, an application,video or music contents, etc., a prediction for popularity aboutspecific content, and a prediction of a preferred motion of a user forthe master apparatus 100 and the slave apparatus 200. Furtherdescription related to generating and training the prediction model willbe made below.

Referring to FIG. 1, the master apparatus 100 may include one or morecomputing apparatuses 100-1, 100-2, and 100-3. That is, the masterapparatus 100 may be implemented using a single computing apparatus100-1, or may be implemented two or more computing apparatuses 100-2 and100-3. In the case of using two or more computing apparatuses 100-2 and100-3, the computing apparatuses 100-2 and 100-3 may be communicablyconnected to each other. The computing apparatuses 100-1, 100-2, and100-3 may include an electronic apparatus capable of performingoperations and processing, and may be implemented using, such as, forexample, a typical desktop computer, a laptop computer, and a servercomputer. Depending on example embodiments, the computing apparatuses100-1, 100-2, and 100-3 may include an electronic apparatus, such as,for example, a smartphone, a tablet personal computer (PC), and homeappliances connected to Internet of Things (IoT).

The slave apparatus 200 may receive the prediction model from the masterapparatus 100 and process a predetermined (or, alternatively, desired)task or operation using the received prediction model. For example, theslave apparatus 200 may predict the popularity of content or a userdemand for the content. Also, the slave apparatus 200 may receive acontrol command from the master apparatus 100 and operate in response tothe received control command.

The slave apparatus 200 may collect various types of local data from asurrounding environment and transmit the collected local data to themaster apparatus 100. Depending on example embodiments, the slaveapparatus 200 may train the prediction model received from the masterapparatus 100 independently of the master apparatus 100. That is, bothof the master apparatus 100 and the slave apparatus 200 may be providedto process machine learning. In this case, the slave apparatus 200 maytransmit the independently trained prediction model to the masterapparatus 100. Information on the trained prediction model may include,for example, a weight and a gradient of the prediction model. In thiscase, when the master apparatus 100 receives local data from the slaveapparatus 200 and/or the trained prediction model from the slaveapparatus 200, the master apparatus 100 may update an existingprediction model by further training the existing prediction model basedon the local data and/or the trained prediction model received from theslave apparatus 200.

The slave apparatus 200 may include one or more slave apparatuses 200-1,200-2, 200-3, 200-4, and 200-5. In the case of using two or more slaveapparatuses 200-1, 200-2, 200-3, 200-4, and 200-5, the slave apparatuses200-1, 200-2, 200-3, 200-4, and 200-5 may be the same type ofapparatuses or different types of apparatuses. Also, a portion of theslave apparatuses 200-1, 200-2, 200-3, 200-4, and 200-5 may be the sametype of apparatuses and another portion thereof may be different typesof apparatuses.

According to an example embodiment, the slave apparatus 200 may include,for example, a base station and a small cell station. For example, theslave apparatuses 200-1, 200-2, 200-3, 200-4, and 200-5 may include asmartphone, a vehicle, an electronic device, such as a navigationdevice, installed in the vehicle, a computer apparatus such as a desktopcomputer, a laptop computer, and a server computer, and home appliances,such as a digital television (TV) and a refrigerator, connected to IoT.In addition, the slave apparatus 200 may include, for example, a tabletPC, a smart watch, a head mounted display (MHD) device, a set-top box, apersonal digital assistant (PDA), a portable game device, an electronicwhiteboard, an electronic billboard, a sound player, and an automatedteller machine (ATM). Also, in addition thereto, the slave apparatus 200may include at least one of apparatuses capable of collecting data andperforming operation processing depending on a selection of a designer.

Each of the master apparatus 100 and the slave apparatus 200 maytransmit and receive data over a communication network 90. Thecommunication network 90 may include a wired communication network, awireless communication network, or a combination of the wiredcommunication and the wireless communication network. Here, the wiredcommunication network may include a communication network constructedusing a cable that is provided using, alone or in combination, forexample, a pair cable, a coaxial cable, an optical fiber cable, and anEthernet cable. The wireless communication network may be implementedusing, alone or in combination, a near field communication network and along distance communication network. The near field communicationnetwork may be implemented based on an available near fieldcommunication network, such as, for example, wireless fidelity (WiFi),WiFi direct, Bluetooth, Bluetooth low energy (BLE), control area network(CAN) communication, ZigBee communication, and near field communication(NFC). The long distance communication network may be implemented basedon an available mobile communication standard, for example, 3rdGeneration Partnership Project (3GPP) such as evolved high speed packetaccess (HSPA+) and Long Term Evolution (LTE), 3GPP2 such as codedivision multiple access (CDMA2000), Worldwide Interoperability forMicrowave Access (WiMAX), and wireless broadband (WiBro).

FIG. 2 is a block diagram illustrating an example of a prediction modeltraining management system according to an example embodiment.

Referring to FIG. 2, the master apparatus 100 of the prediction modeltraining management system 1 may include a collector 101, a processor110, and a storage 120. The slave apparatus 200 may include acommunicator 202 and a processor 210. Also, the slave apparatus 200 mayfurther include at least one of a collector 201 and a storage 220, ifnecessary.

The collector 101 of the master apparatus 100 may collect at least onepiece of data used to generate and train a model.

For example, the collector 101 may collect various types of data. Inthis case, the collector 101 may include a communicator 102 configuredto communicate with the communicator 202 of the slave apparatus 200. Thecommunicator 102 may include a communication apparatus capable ofreceiving data over a communication network, which is described above.The communicator 102 may receive various types of data collected by theslave apparatus 200 from the slave apparatus 200. Also, the collector101 may extract data from a database (not shown) constructed in advanceseparately, and may collect the extracted data. In addition, thecollector 101 may include an input device, for example, a keyboard, amouse, a touchscreen, a global positioning system (GPS) receiver, apunched card, an external memory slot, a camera, a microphone, and adevice for receiving a variety of external information collected over anInternet network. The collector 101 may also collect data under controlor operation of the input device.

Data collected by the collector 101 is not limited thereto. For example,data may include a command or an instruction selected by the user,content requested by the user, meta information, such as a category andclass, of the selected command/instruction or the requested content, anumber of times the command/instruction or the content is selected orrequested, a date/season/time at which the command/instruction or thecontent is selected or requested, a data point around data to bepredicted or a number of data points, and various items that may be usedby the designer for prediction.

The variety of information collected by the collector 101 may bedelivered to the processor 110 through an electronic circuit, a cable,and the like.

The processor 110 is configured to train and manage a prediction modeland to perform various types of processing associated therewith. Also,the processor 110 is configured to generate and train the predictionmodel and to perform an operation based on the prediction model, forexample, prediction for a request or a selection of the user.

In detail, for example, the processor 110 may control the collector 101to extract data from the database, and to transfer a data transmissioncommand to the slave apparatus 200 to transmit the collected data to themaster apparatus 100. Alternatively, the processor 110 may call anecessary learning model from a model dictionary 121 of FIG. 3, maygenerate various types of prediction models or train a prediction modelusing various types of machine learning algorithms, and may transmit thegenerated prediction model or the trained prediction model to the slaveapparatus 200. The processor 110 may determine and obtain a predictionresult using the generated prediction model or the trained predictionmodel. Also, if necessary, the processor 110 may perform an operationcorresponding to the prediction result. Alternatively, the processor 110may transmit the prediction result to the slave apparatus 200, or maygenerate a command corresponding to the prediction result and transmitthe generated command to the slave apparatus 200. Additionally, theprocessor 110 may control the overall operation of the master apparatus100. Generation and training of the prediction model by the processor110 is further described below.

The processor 110 may execute an application stored in a storage 120 toperform preset operation, determination, processing, and/or controloperations. Here, the application stored in the storage 120 may begenerated in advance by the designer and stored in the storage 120, ormay be acquired or updated through an electronic software distributionnetwork that is accessible over a wired or wireless communicationnetwork.

The processor 110 may be implemented using at least one electronicapparatus, such as, for example, a central processing unit (CPU), amicro controller unit (MCU), a microprocessor (Micom), an applicationprocessor (AP), an electronic controlling unit (ECU), and various typesof other electronic apparatuses capable of performing various types ofoperation processing and generating a control signal. The apparatusesmay be implemented using, for example, at least one semiconductor chipand relevant part.

The storage 120 is configured to transitorily or non-transitorily recordand store a variety of applications and data required for operations ofthe processor 110 or desired to be recorded.

For example, referring to FIG. 3, the storage 120 may store the modeldictionary 121, collected at least one piece of data 123 including rawdata or preprocessed data, at least one generated or obtained predictionmodel 125, at least one trained prediction model 127, and/or a varietyof information associated therewith. The variety of informationassociated therewith may include, for example, raw data andconfiguration information of a generated model or a trained model. Also,in the case of storing the prediction model 125 and the trainedprediction model 127, the storage 120 may store, alone or incombination, hyper parameters of the prediction model 125 and thetrained prediction model 127. The hyper parameter may include, forexample, a number of hidden layers and a learning rate, as a parameterset to execute a machine learning.

The storage 120 may be implemented using one or more physical storagemediums, for example, a hard disk device. The aforementioned data andmodels may be stored in a single physical storage medium and may bedistributively stored in a plurality of physical storage mediums. Ifnecessary, a specific storage medium may be assigned to store each pieceof and/or model.

The storage 120 may include at least one of a main memory and anauxiliary memory. The main memory may be implemented using semiconductorstorage media, for example, read only memory (ROM) and random accessmemory (RAM). Examples of ROM may include typical ROM, erasableprogrammable read only memory (EPROM), electrically erasable andprogramable read only memory (EEPROM), and mask-ROM. Examples of RAM mayinclude dynamic random access memory (DRAM) and static random accessmemory (SRAM). The auxiliary memory may be implemented using at leastone storage media capable of permanently or semi-permanently storingdata, such as, for example, a flash memory device, a secure digital (SD)card, a solid state drive (SSD), a hard disc drive (HDD), a magneticdrum, a compact disc (CD), optical media such as DVD or laser disc, amagnetic tape, magneto-optical media, and floppy disk.

According to an example embodiment, the communicator 202 of the slaveapparatus 200 is configured to communicate with the communicator 102 ofthe master apparatus 100. The communicator 202 may receive a controlcommand, data, and/or a prediction model from the communicator 102 ofthe master apparatus 100. Also, the communicator 202 may transmit, tothe communicator 102 of the master apparatus 100, data collected by thecollector 201, a prediction model generated directly or updated throughtraining, information associated therewith, and/or a feedback signalcorresponding to the control command The communicator 202 of the slaveapparatus 200 may be implemented using a communication apparatus capableof receiving data over a communication network, which is describedabove.

The collector 201 of the slave apparatus 200 may collect various typesof data and transfer such collected information to the processor 210.For example, the collector 201 may obtain necessary data from varioustypes of devices connected to the slave apparatus 200 and transfer theobtained information to the processor 210. Here, the various types ofdevices may refer to apparatuses capable of collecting a variety ofinformation, such as, for example, a keyboard, a touchscreen, anexternal memory slot, a GPS receiver, a camera, a microphone, a steeringwheel, a thermometer, and a hygrometer. Also, the collector 201 maycollect a variety of external information received through thecommunicator 202 and transfer the collected external information to theprocessor 210. For example, the collector 201 may collect information ofa base station, for example, a cache hit value or a count valueindicating a number of times each piece of content is requested.

The processor 210 may perform various types of operation processingrequired for the overall operation of the slave apparatus 200 or maygenerate a control signal associated therewith. Depending on exampleembodiments, the processor 210 may perform operation processing andcontrol operations in response to a control command generated by theprocessor 110 of the master apparatus 100.

According to an example embodiment, the processor 210 may generate andtrain a prediction model independently of the processor 110 of themaster apparatus 100. In this case, the processor 210 may train theprediction model transmitted from the master apparatus 100 based on datacollected by the collector 201 of the slave apparatus 200. Accordingly,the prediction model transmitted from the master apparatus 100 may beupdated As described above, training of the prediction model by theprocessor 210 may be performed independently of the processor 110 of themaster apparatus 100. Depending on example embodiments, training of theprediction model by the processor 210 may be performed using the samemethod as one used by the processor 110 of the master apparatus 100 orusing a method different therefrom.

Similar to the processor 110 of the master apparatus 100, the processor210 may be implemented using at least one electronic apparatus.

The storage 220 may store various a variety of data or applicationsrequired for operations of the processor 210. For example, the storage220 may transitorily or non-transitorily store a prediction modeltransferred from the master apparatus 100, and may transitorily ornon-transitorily store a prediction model generated or trained by theprocessor 210.

Similar to the storage 120 of the storage 120, the storage 220 mayinclude at least one of a main memory and an auxiliary memory, and maybe implemented using, for example, a semiconductor and a magnetic disc.

Hereinafter, an example embodiment of generating and updating aprediction model in the master apparatus 100 and/or the slave apparatus200 will be described with reference to FIGS. 3 through 5. In thefollowing, a prediction model generation and training process performedby the master apparatus 100 according to an example embodiment will bedescribed, which may be applicable to a prediction model generation andtraining process performed by the salve apparatus 200 in the same manneror through partial modifications. To avoid repetition, a furtherdescription related to the prediction model generation and/trainingprocess performed by the slave apparatus 200 is omitted herein.

FIG. 3 is a block diagram illustrating an example of a master apparatusaccording to an example embodiment.

Referring to FIG. 3, the processor 110 may include an artificialintelligence manager 111, a data processing 112, a model generator 114,a model trainer 116, and a predictor 118. The artificial intelligencemanager 111, the data processing 112, the model generator 114, the modeltrainer 116, and the predictor 118 may be logically distinguished fromeach other or may be physically separate from each other. In the lattercase, at least two of the artificial intelligence manager 111, the dataprocessing 112, the model generator 114, the model trainer 116, and thepredictor 118 may be implemented by different physical processors, forexample, central processing units.

The artificial intelligence manager 111 is configured to control theoverall operation or a portion of operation of the data processing 112,the model generator 114, the model trainer 116, and the predictor 118.For example, the artificial intelligence manager 111 may control themodel generator 114 to generate or select appropriate data or predictionmodel, or may control the model trainer 116 to select and train theappropriate prediction model. Depending on example embodiments, theartificial intelligence manager 111 may perform and/or control trainingand selection of an optimal prediction model based on feedbackinformation, for example, information on accuracy of a model or alearning loss of the model, transferred from at least one of the modeltrainer 116 and the predictor 118.

The data processing 112 may performing, for example, selection,extraction, processing, and deformation, on data, for example, a cachehit value or a count value indicating a number of times each piece ofcontent is requested, collected by the data collector 101, or maygenerate a data set based thereon and transfer the data set to the modelgenerator 114. Here, the collected data may refer to the data 123 thatis stored in the storage 120. For example, the data processing 112 mayclean a memory and may extract a log file from data. As another example,the data processing 112 may generate feature information by combining aplurality of pieces of data. The data or the data set processed by thedata processing 112 may be transferred to the model generator 114 and/orthe model trainer 116.

The model generator 114 may generate and obtain various types ofprediction models. Here, the prediction model may be implemented usingat least one of a multilayer perceptron (MLP), a deep neural network(DNN), a convolutional neural network (CNN), a recurrent neural network(RNN), a convolutional recurrent neural network (CRNN), a deep beliefnetwork (DBN), and a deep Q-network. In addition thereto or inreplacement thereof, the prediction model may include various types ofneural networks used for machine learning. If necessary, the modelgenerator 114 may use the data or the generated data set processed bythe data processing 112 for model generation. Also, the prediction modelgenerated by the model generator 114 may refer to the prediction model125 that is transitorily or non-transitorily stored in the storage 120.

As described above, to generate various types of prediction models, themodel generator 114 may use the model dictionary 121 stored in thestorage 120. The model dictionary 121 may refer to a data setconstructed using rules and frames about various types of deep learningmodels, and is configured to provide at least one model to the modelgenerator 114 in response to reading or search by the model generator114.

The model trainer 116 may train the prediction model 125 that isgenerated by the model generator 114 using the data and/or data settransmitted from the data processing 112. In this case, the modeltrainer 116 may call the prediction model 125 stored in the storage 120,and may train the called prediction model 125 based on the obtained dataand/or data set. A processing result of the model trainer 116 may betransferred to the storage 120. The storage 120 may store the trainedprediction model 127 obtained from the model trainer 116, and may storea variety of information, for example, model configuration information,training accuracy and validation accuracy, associated with the trainedprediction model 127. Here, such information may be transitorily ornon-transitorily stored.

The predictor 118 may access the trained prediction model 127 stored inthe storage 120, perform prediction based on the trained predictionmodel 127, and obtain a prediction result. Depending on exampleembodiments, the predictor 118 may store the prediction result in thestorage 120, and may also transmit the prediction result to theartificial intelligence manager 111. Also, the predictor 118 may storeinformation associated with the prediction, for example, predictionaccuracy, in the storage 120, and may transmit information associatedwith the prediction to the artificial intelligence manager 111.According to an example embodiment, the artificial intelligence manager111 may verify the prediction result or information associated with theprediction, for example, prediction accuracy, stored by the predictor118 through access to the storage 120 or directly receive the predictionresult or information associated with the prediction from the predictor118 and perform a predetermined (or, alternatively, desired) operationbased on the received prediction result or information associated withthe prediction. For example, based on the prediction result orinformation associated with the prediction, the artificial intelligencemanager 111 may generate a control command for at least one of themaster apparatus 100 and the slave apparatus 200 and may control atleast one of the master apparatus 100 and the slave apparatus 200 usingthe control command. According to an example embodiment, the artificialintelligence manager 111 may determine whether to further proceed withtraining or suspending the training based on information, for example,prediction accuracy. When the artificial intelligence manager 111determines to suspend the training, a prediction model trained by apoint in time at which the training is suspended may be stored in thestorage 120. In this case, the prediction model trained by the point intime may be stored to be distinguished from the existing predictionmodel 125 or the other trained prediction model 127.

To retrieve an optimal prediction model, the processor 110 may detectthe optimal prediction model using various methods. According to anexample embodiment, the processor 110, for example, at least one of theartificial intelligence manager 111, the model generator 114, and themodel trainer 116, may use a predetermined (or, alternatively, desired)search method to detect an optimal prediction model. The search methodmay include, for example, at least one of a grid search method and arandom search method. The grid search method refers to a method ofsearching for an optimal model by sequentially increasing a value of ahyper parameter. The random search method refers to a method of randomlysearching for an optimal model to reduce a search space.

In detail, the processor 110 may select a predetermined (or,alternatively, desired) candidate model. The candidate model mayinclude, for example, a CNN, an RNN, and a CRNN. The processor 110generates a model based on the search method, for example, the randomsearch method.

According to an example embodiment, to minimize training/learning loss,the processor 110 may select an optimizer configured to calculate agradient. The optimizer may use, for example, gradient descent,stochastic gradient descent, and adaptive moment estimation. Forexample, the adaptive moment estimation may be used as the optimizer tocalculate a gradient of the CNN. In this case, a backpropagationalgorithm may be further used in addition to the adaptive momentestimation. In the case of using long short-term memory (LSTM) modelsand/or regions with CNN features (R-CNN), truncated backpropagationthrough time (TBTT) may be further used to calculate a gradient. TheTBTT may be used with the aforementioned adaptive moment estimation. TheTBTT is enhanced from a backpropagation through time (BPTT) algorithmfor RNN. In the TBTT, sequences are processed each one stage at a timeand the BPTT algorithm is conversely updated during a fixed number oftime slots.

As described above, the processor 110 may generate and train aprediction model and obtain a prediction result corresponding thereto.The processor 110 may predict an operation of the user, for example, auser demand for content, based on the obtained prediction result, andmay appropriately and effectively manage various resources, for example,power to be used, a cache resource, and a computing resource, in themaster apparatus 100 and the slave apparatus 200, based on the predictedoperation.

FIG. 4 illustrates an example of obtaining a prediction result accordingto an example embodiment.

A prediction model 300 of FIG. 4 may be used by either the masterapparatus 100 or the slave apparatus 200. Additionally, the predictionmodel 300 may be used by all of the master apparatus 100 and the slaveapparatus 200. In the latter case, each of the master apparatus 100 andthe slave apparatus 200 may independently use the prediction model 300.

Referring to FIG. 4, the prediction model 300 may include two predictionmodels, for example, a first prediction model 310 and a secondprediction model 320.

The first prediction model 310 may be configured to predict, forexample, a class. In detail, the first prediction model 310 may be usedto predict a multi-class label.

The first prediction model 310 may include two algorithms, for example,a first algorithm 311 and a second algorithm 313. The first algorithm311 and the second algorithm 313 sequentially process data. In detail,data 301 that is input to the first prediction model 310 is input to thefirst algorithm 311 and output data 302, for example, convolution resultdata, of the first algorithm 311 is sequentially input to the secondalgorithm 313. Depending on example embodiments, a result value, forexample, class prediction data, of the second algorithm 313 may beprocessed by a fully connected layer (FCL) 315 and output through anoutput end 316. Therefore, the first prediction model 310 may acquireclass prediction data 319. The acquired class prediction data 319 may beinput to the second prediction model 320.

Each of the first algorithm 311 and the second algorithm 313 may use atleast one of machine learning algorithms, for example, a neural network.For example, each of the first algorithm 311 and the second algorithm313 may include a MLP, a DNN, a CNN, an RNN, a CRNN, a DBN, and a deepQ-network. Depending on example embodiments, the first algorithm 311 andthe second algorithm 313 may be implemented using the same neuralnetwork or may be implemented using different neural networks.

For example, the first algorithm 311 may be implemented using a CNN andthe second algorithm 313 may be implemented using an RNN. In this case,the data 301 is input to and then convolution-processed by the CNN ofthe first algorithm 311. Accordingly, the output data 302, for example,convolution result data, corresponding to the input data 301 isacquired. Depending on example embodiments, the convolution result datamay be transferred to the second prediction model 320.

Once the convolution result data corresponding to the output data 302 isacquired, the convolution result data is input to the RNN of the secondalgorithm 313. The RNN of the second algorithm 313 may be speciallydesigned for temporal dynamics of input sequential data. The RNN of thesecond algorithm 313 may include a plurality of RNN cells 314 that isconnected to each other or disconnected. Depending on exampleembodiment, the RNN cell 314 may include, for example, a gated recurrentunit and long short-term memory. Data processed and output by the RNN ofthe second algorithm 313 is input to the fully connected layer 315.

The fully connected layer 315 may classify a class label of the inputdata 301 and the output end 316 may output result data, for example, theclass prediction data 319. Here, the class prediction data 319 may be amulti-class label. The multi-class label may include a stochastic value.According to an example embodiment, cross-entropy may be used toclassify a class label. Cross-entropy loss is general in a log-linearmodel and a neural network, and used to predict distribution ofavailable labels. In the case of using the cross-entropy loss, output ofthe fully connected layer 315 may be transformed using a predetermined(or, alternatively, desired) transformation. For example, the output ofthe fully connected layer 315 may be transformed using softmaxtransformation or may be assumed to be transformed using the same. Theclass prediction data 319 may be transferred to the second predictionmodel 320.

Through the above process, the first prediction model 310 may output theclass prediction data 319 learned by the CNN of the first algorithm 311and the RNN of the second algorithm 313 to be transferred to the secondprediction model 320.

The second prediction model 320 may be configured to obtain a predictionresult. For example, the second prediction model 320 may be configuredto predict a number of requests. Also, the second prediction model 320may be configured to be suitable for various types of predictions. Thatis, the second prediction model 320 may be designed for a predictiondesired by a designer based on an arbitrary selection of the designer.

The second prediction model 320 receives data 302 a from the firstprediction model 310 and processes the received data 302 a. According toan example embodiment, the data 302 a may be the output data 302 of thefirst algorithm 311, for example, the convolution result data, or may bethe class prediction input data 319. Also, the received data 302 a mayinclude all of the convolution result data and the class prediction data319.

The second prediction model 320 may include a third algorithm 321configured to process the received data 302 a. The third algorithm 321may be configured using at least one of the aforementioned machinelearning algorithms For example, the third algorithm 321 may beimplemented using an RNN. The RNN used as the third algorithm 321 may bespecially designed for temporal dynamics of input sequential data andmay include a plurality of RNN cells 322.

Result data processed by the second prediction model 320, for example,the RNN is output to an output end 329. Here, the result data includesdata about a desired prediction result, for example, a number ofrequests. Therefore, a prediction result about a target desired by thedesigner may be obtained. The processor 110 of FIG. 2, for example, theartificial intelligence manager 111 of the processor 110 mayappropriately manage at least one master apparatus 100 and/or at leastone slave apparatus 200 based on the obtained prediction result.

The second prediction model 320 may obtain a loss using a methodselected by the designer. For example, the second prediction model 320may measure a loss for a result, for example, a prediction for a numberof requests, using a mean square error (MSE). The MSE may further easilycalculate a gradient with an excellent mathematical characteristic.

Hereinafter, data, for example, the data 302, 302 a, used by theprediction model 300 will be further described.

FIG. 5 illustrates an example of sample data according to an exampleembodiment. In FIG. 5, each of t₁ to t_(n) denotes a time slot and ndenotes a number of a corresponding time slots.

As described above, in the prediction model 300, a variety of data, forexample, sample data is input to the first algorithm 311, the secondalgorithm 313, and the third algorithm 321. For example, in the firstprediction model 310, the data 301 may be input to the first algorithm311 and the convolution result data corresponding to the output data 302may be input to the second algorithm 313. In the second prediction model320, the data 302 a may be input to the third algorithm 321.

The sample data, for example, the data 301, the convolution result datacorresponding to the output data 302, and the data 302 a, used in theprediction model 300 may be configured based on a tensor in which datais multi-dimensionally arranged as shown in FIG. 5. The tensor may beobtained at each specific point in time slot (t₁ to t_(n)). That is,data may be input or generated in a form of the tensor in each of timeslots t₁, t_(n), and t_(m) therebetween. Here, t_(m) denotes a time slotbetween t₁ and t_(n), and 1<m<n. The tensor may be a set of an i×j×knumber of pieces of data. In detail, the tensor may include i×j×k slotscapable of including data, for example, a number of requests forspecific content or a number of selections on a specific command.Desired data may be recorded or not recorded in each slot. If the tensorrelates to feature information, the tensor may include desired data,such as a type of a feature (e.g., a name of content, a number ofrequests, and a grade of content if the content is movie), or a datapoint around data to be predicted. In this case, i may be defined todenote a number of input data values for each feature type. Also, j maybe defined to denote a number of types of features and k may be definedto denote a number of data points around data. It is provided as anexample only, and thus the tensor may be variously defined based on anarbitrary selection of the designer.

Hereinafter, a method (hereinafter, also referred to as a predictionmodel training management method) of training and managing a predictionmodel according to an example embodiment will be described withreference to FIGS. 6 and 7.

FIG. 6 illustrates an example of a prediction model training managementmethod according to an example embodiment.

The prediction model training management method may be performed basedon the aforementioned prediction model training management system. Thatis, the prediction model training management method may be performed byat least one of a master apparatus and a slave apparatus. Describing anexample embodiment of the prediction model training management method ofFIG. 6, at least one of the master apparatus and the slave apparatus maycollect a variety of data, for example, information, required for orassociated with prediction. In this case, the master apparatus and theslave apparatus may collect data independently or through cooperation.Depending on example embodiments, one of data collection operation 20 bythe master apparatus and data collection operation 30 by the slaveapparatus may be omitted.

In operation 31, the slave apparatus may transmit the collected data tothe master apparatus and the master apparatus may receive the data.

In operation 21, the master apparatus may obtain and generate aprediction model based on at least one of data directly collected by themaster apparatus and the data received from the slave apparatus. Here,the master apparatus may use either the data collected by the masterapparatus or the data received from the slave apparatus.

In operation 22, the prediction model obtained by the master apparatusmay be transmitted to the slave apparatus. In operation 32, once theprediction model is received, the slave apparatus may perform varioustypes of operations, for example, a prediction of demand, using thereceived prediction model. The slave apparatus may also transmitinformation on a result of using the prediction model to the masterapparatus. For example, in operation 33, the slave apparatus maytransmit information on accuracy of the prediction model to the masterapparatus. In operation 24, the master apparatus may use the receivedinformation to train the prediction model. Also, in operation 23, themaster apparatus may use the generated and obtained prediction model, ifnecessary. Depending on example embodiments, operations 22, 23, and 32may be omitted.

In operation 24, the master apparatus may train the prediction model. Indetail, the master apparatus may train the generated prediction modeland may store a setting value, training accuracy, and validationaccuracy of the prediction model.

Once the prediction model is trained, the master apparatus may transmitthe trained prediction model to the slave apparatus in operation 25 andthe slave apparatus may use the trained prediction model or may furthertrain the prediction model if necessary in operation 36. Also, the slaveapparatus may update an existing stored trained model.

In operation 34, the slave apparatus may also train the predictionmodel. In detail, in operation 34, the slave apparatus may train theprediction model that is transmitted from the master apparatus inoperation 22 based on the data that is directly collected by the slaveapparatus in data collection operation 30. Therefore, the slaveapparatus may obtain the trained prediction model. In operation 35, theprediction model trained by the slave apparatus may be transmitted tothe master apparatus. Depending on a selection of a designer, the slaveapparatus may transmit information associated with the prediction model,for example, a weight and a gradient, to the master apparatus instead oftransmitting the prediction model thereto.

Once the trained prediction model is received from the slave apparatusin operation 35, the master apparatus may update the existing trainedprediction model based on the received trained model prediction inoperation 26. The trained prediction model that is received from thesalve apparatus in operation 35 may be discarded without being updatedbased on settings of the master apparatus. If necessary, the masterapparatus may further train the trained prediction model that isreceived the slave apparatus and/or may use the same to perform adesired operation.

Through the above process, the prediction model may be trained by atleast one of the master apparatus and the slave apparatus, therebyfurther enhancing the accuracy of the prediction model.

FIG. 7 is a flowchart illustrating a process of obtaining a predictionresult according to an example embodiment.

The process of obtaining a prediction result of FIG. 7 may be performedby at least one of a master apparatus and a slave apparatus. Also, aportion of the process may be performed by the master apparatus andanother portion of the process may be performed by the slave apparatus.

Referring to FIG. 7, in operation 51, data may be generated. The datamay include sample data in a tensor form of FIG. 5. The data may beinput to a prediction model. Here, the prediction model may include afirst prediction model and a second prediction model. In this case, asdescribed above with reference to FIG. 4, the data may be initiallyinput to the first prediction model.

In operation 52, the first prediction model may process the input databy applying at least one algorithm. The first prediction model may referto a model to predict a class, for example, a multi-class label. Thealgorithm may include a neural network. Also, the first prediction modelmay be implemented using two neural networks that are sequentially used.For example, the first prediction model may be implemented using a CNNand an RNN. In this case, the data may be input to the CNN andconvolutionally processed and then input to the RNN. Also, the firstprediction model may include a fully connected layer (FCL) forclassifying a class label. A value output from the RNN may be input tothe fully connected layer and the fully connected layer may output classprediction data. Thus, the first prediction model may output final classprediction data.

The second prediction model may include a model configured to obtain aprediction result. In operation 53, the second prediction model mayprocess the class prediction data output from the first prediction modelby applying at least one algorithm thereto. The second prediction modelmay input the class prediction data to the RNN and may output resultdata. Here, the result data may include a prediction result about atarget desired by the designer. That is, the result data may include afinal prediction result.

In operation 54, an appropriate prediction result may be obtained.

The prediction model training management methods and/or the predictionresult obtaining methods according to the example embodiments may beimplemented in a form of a program executable by a computer apparatus.For example, the program may include, alone or in combination withprogram instructions, data files, data structures, and the like. Theprogram may be designed and manufactured using a machine code or ahigher level code. The program may be specially designed to implementthe example embodiments and may be implemented using functions(including a library function) kind well-known and available to thosehaving skill in the computer software arts. Also, a computer apparatusin which the program is executable may include a processor, a memory,and the like to implement functions of the program.

The program for implementing the example embodiments may be recorded innon-transitory computer-readable media. Examples of the non-transitorycomputer-readable media may include magnetic media such as hard discsand floppy discs; optical media such as CD-ROM discs and DVDs;magneto-optical media such as floptical discs; and hardware apparatusthat are specially configured to store and perform a specific programexecuted in response to call of a computer, such as ROM, RAM, flashmemory.

At least one of the prediction model training management system 1, themaster apparatus 100, the slave apparatus 200, and the prediction modeltraining management method may be adopted alone or in combination invarious fields. For example, the prediction model training managementsystem 1, the master apparatus 100, the slave apparatus 200 and/or theprediction model training management method may be used to implement avariety of techniques such as a video on demand (VOD) service, a videostreaming service, edge computing, intelligent network management, avirtualized network, and IoT. In detail, the aforementioned systems,apparatuses, and methods may be used for content caching of thetechniques.

A number of example embodiments regarding the prediction model trainingmanagement system, the prediction model training management method, andthe master apparatus and the slave apparatus for the same have beendescribed above. Nonetheless, it should be understood that variousmodifications may be made to these example embodiments. For example,various apparatuses or methods achieved by one of ordinary skill in theart through alterations and modifications thereto may be an exampleembodiment of the prediction model training management system, theprediction model training management method, and the master apparatusand the slave apparatus for the same. For example, suitable results maybe achieved if the described techniques are performed in a differentorder and/or if components in a described system, architecture,apparatus, or circuit are combined in a different manner and/or replacedor supplemented by other components or their equivalents. Accordingly,other implementations are still within the scope of the followingclaims.

What is claimed is:
 1. A system for training and managing a predictionmodel, the system comprising: a master apparatus configured to generatea prediction model, train the prediction model, and obtain the trainedprediction model; and a slave apparatus configured to collect data,transmit the data to the master apparatus, receive the prediction modelor the trained prediction model from the master apparatus, and operatebased on the prediction model or the trained prediction model, whereinthe master apparatus is further configured to generate the predictionmodel or train the prediction model based on the data transmitted fromthe slave apparatus.
 2. The system of claim 1, wherein the predictionmodel comprises: a first prediction model configured to obtain aprediction result about a class corresponding to input data; and asecond prediction model configured to receive a result of the firstprediction model and obtain a prediction result corresponding to theresult of the first prediction model.
 3. The system of claim 2, whereinthe first prediction model comprises a first algorithm to which data isinput and a second algorithm to which an output result of the firstalgorithm is input.
 4. The system of claim 3, wherein each of the firstalgorithm and the second algorithm comprises at least one of machinelearning algorithms comprising at least one of a multilayer perceptron(MLP), a deep neural network (DNN), a convolutional neural network(CNN), a recurrent neural network (RNN), a convolutional recurrentneural network (CRNN), a deep belief network (DBN), and a deepQ-network.
 5. The system of claim 3, wherein the input data is a tensorin which data is arranged in a multidimensional form.
 6. The system ofclaim 5, wherein the tensor comprises a type of a feature and a datapoint around data to be predicted.
 7. The system of claim 1, wherein theslave apparatus is further configured to train the prediction model orthe trained prediction model independently of the master apparatus andtransmit the training result to the master apparatus.
 8. The system ofclaim 7, wherein the master apparatus is further configured to updatethe trained prediction model based on the training result.
 9. The systemof claim 1, wherein at least one of the master apparatus and the slaveapparatus comprises: a data processing configured to perform at leastone processing of the collected data and generate a data set based onthe collected data; a model generator configured to generate at leastone prediction model; a model trainer configured to train the at leastone prediction model generated by the model generator based on the dataor the data set transferred by the data processing; a predictorconfigured to obtain a prediction result using at least one of theprediction model generated by the model generator and the predictionmodel trained by the model trainer; and an artificial intelligencemanager configured to control and manage at least one of the dataprocessing, the model generator, the model trainer, and the predictor.10. A master apparatus comprising: a communicator configured tocommunicably connect to a slave apparatus and receive data from theslave apparatus; and a processor configured to generate a predictionmodel, train the prediction model, and obtain the trained predictionmodel, wherein the communicator is further configured to transmit theprediction model or the trained prediction model to the slave apparatus.11. The master apparatus of claim 10, wherein the processor comprises: adata processing configured to perform at least one processing ofcollected data and generate a data set based on the collected data; amodel generator configured to generate at least one prediction model;and a model trainer configured to train the at least one predictionmodel generated by the model generator based on the data or the data settransferred by the data processing.
 12. The master apparatus of claim11, wherein the prediction model comprises: a first prediction modelconfigured to obtain a prediction result about a class corresponding toinput data; and a second prediction model configured to receive a resultof the first prediction model and obtain a prediction resultcorresponding to the result of the first prediction model.
 13. Themaster apparatus of claim 12, wherein the first prediction modelcomprises a first algorithm to which data is input and a secondalgorithm to which an output result of the first algorithm is input. 14.The master apparatus of claim 13, wherein each of the first algorithmand the second algorithm comprises at least one of machine learningalgorithms comprising at least one of a multilayer perceptron (MLP), adeep neural network (DNN), a convolutional neural network (CNN), arecurrent neural network (RNN), a convolutional recurrent neural network(CRNN), a deep belief network (DBN), and a deep Q-network.
 15. Themaster apparatus of claim 11, wherein the processor further comprises: apredictor configured to obtain a prediction result using at least one ofthe prediction model generated by the model generator and the predictionmodel trained by the model trainer.
 16. The master apparatus of claim15, wherein the processor further comprises: an artificial intelligencemanager configured to control and manage at least one of the dataprocessing, the model generator, the model trainer, and the predictor.17. A slave apparatus comprises: a communicator configured to receive aprediction model or a trained prediction model from a master apparatus;and a processor configured to generate a control command based on theprediction model or the trained prediction model, wherein the processoris further configured to train the prediction model or the trainedprediction model, and the communicator is further configured to transmitthe training result to the master apparatus.
 18. The slave apparatus ofclaim 17, wherein the prediction model comprises: a first predictionmodel configured to obtain a prediction result about a classcorresponding to input data; and a second prediction model configured toreceive a result of the first prediction model and obtain a predictionresult corresponding to the result of the first prediction model.
 19. Amethod of training and managing a prediction model, the methodcomprising: collecting, by at least one of a master apparatus and aslave apparatus, data; generating, by at least one of the masterapparatus and the slave apparatus, a prediction model based on the data;training, by at least one of the master apparatus and the slaveapparatus, the prediction model and obtaining the trained predictionmodel; and performing, by at least one of the master apparatus and theslave apparatus, a prediction based on at least one of the predictionmodel and the trained prediction model, wherein the prediction modelcomprises: a first prediction model configured to obtain a predictionresult about a class corresponding to input data; and a secondprediction model configured to receive a result of the first predictionmodel and obtain a prediction result corresponding to the result of thefirst prediction model.
 20. The method of claim 19, wherein the firstprediction model comprises a first algorithm to which data is input, anda second algorithm to which an output result of the first algorithm isinput, and the first algorithm and the second algorithm comprise atleast one of machine learning algorithms comprising at least one of amultilayer perceptron (MLP), a deep neural network (DNN), aconvolutional neural network (CNN), a recurrent neural network (RNN), aconvolutional recurrent neural network (CRNN), a deep belief network(DBN), and a deep Q-network.